Sentiment Classification with Support Vector Machines and Multiple Kernel Functions
Lecture notes in computer sciencePublished 1 January 2009
Tanasanee Phienthrakul, Boonserm Kijsirikul, Hiroya Takamura, Manabu Okumura
Citations26
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
This paper analyzes and compares various non-negative linear combination kernels applied on product reviews to determine whether a review is positive or negative and shows that the performance of the combination kernels that outperforms the single kernels.
Abstract
Support vector machine (SVM) is a learning technique that performs well on sentiment classification. The performance of SVM depends on the used kernel function. Hence, if the suitable kernel is chosen, the efficiency of classification should be improved. There are many approaches to define a new kernel function. Non-negative linear combination of multiple kernels is an alternative, and the performance of sentiment classification can be enhanced when the suitable kernels are combined. In this paper, we analyze and compare various non-negative linear combination kernels. These kernels are applied on product reviews to determine whether a review is positive or negative. The results show that the performance of the combination kernels that outperforms the single kernels.
Keywords
Computer Science
TechnometricsStatistical Learning Theory
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